What Is Network Traffic Analysis (NTA)?
Network Traffic Analysis (NTA) is the process of monitoring, capturing, and analyzing network data to understand network behavior, identify security threats, and optimize performance. It involves examining the flow of information, including patterns, protocols, and volume, to gain insights into how devices communicate and detect anomalies. NTA is crucial for cybersecurity, network performance monitoring, and capacity planning.
In cybersecurity, NTA is a component of Network Detection and Response (NDR) systems. It provides visibility into network activity, enabling security teams to detect and respond to threats. NTA helps organizations understand how information moves across the network, identify threat sources, and optimize network performance.
NTA enables security professionals and network administrators to identify policy violations, monitor bandwidth usage, and verify compliance with internal and regulatory standards. By building an ongoing picture of normal network behavior, NTA helps set a baseline against which unusual or dangerous patterns can be quickly identified.
Common NTA techniques include:
- Packet capture and inspection: Examining individual network packets to understand their contents and context.
- Protocol analysis: Analyzing the protocols used for communication to identify potential issues.
- Behavioral analysis: Using machine learning and behavioral analytics to detect anomalous network behavior.
- Anomaly detection: Identifying deviations from the established baseline of normal network activity.
- Flow data analysis: Summarizing metadata about network communications.
- Passive monitoring: Observing network traffic unobtrusively to analyze activity and performance without injecting additional data.
- Active monitoring: Simulating traffic or sending probes to test network performance and detect issues proactively.
This is part of a series of articles about network monitoring.
In this article:
- Why Is NTA Important?
- Core Network Traffic Analysis Techniques
- Common Use Cases for Network Traffic Analysis
- Network Traffic Analysis Best Practices
Why Is NTA Important?
Network traffic analysis (NTA) aids in maintaining the security and health of a network. Even with strong firewalls in place, network traffic can still bypass defenses, especially through tactics like tunneling, VPNs, or anonymizing services. Monitoring network traffic allows organizations to detect these threats early and prevent potential breaches.
The increasing prevalence of attacks, such as ransomware, underscores the importance of NTA. Ransomware attacks often exploit vulnerabilities or protocols. For example, the WannaCry attack scanned for networks with open TCP port 445, using SMBv1 vulnerabilities to gain access. NTA can help detect such activity by monitoring traffic patterns and identifying signs of malicious behavior.
Additionally, monitoring internal traffic allows for the validation of firewall rules and the detection of suspicious activity on management protocols, such as Telnet or HTTP. These unencrypted protocols can reveal sensitive information, like user credentials and device configurations, to attackers. By analyzing network traffic, organizations can pinpoint potential threats, enforce security policies, and ensure the network operates securely and efficiently.
Core Network Traffic Analysis Techniques
1. Packet Capture and Inspection
Packet capture and inspection examines the content of individual network packets beyond standard metadata such as headers or flow information. Deep packet inspection (DPI) tools can decode protocols, identify applications, and even inspect the payload for data patterns, malware signatures, or policy violations.
This level of granularity enables organizations to enforce security policies, detect sensitive data leaks, and uncover sophisticated threats hiding within legitimate traffic. While DPI provides insight into network communications, it can be resource-intensive due to the need to process and analyze large volumes of packet data at line rate.
2. Protocol Analysis
Protocol analysis focuses on understanding and verifying the use of network protocols across communication sessions. Analyzers decode protocol conversations such as HTTP, DNS, SMTP, or custom application traffic, allowing administrators to detect protocol violations, misconfigurations, or attempts to tunnel malicious payloads through legitimate services.
By verifying adherence to protocol standards, organizations can identify both performance issues and security risks. Protocol analysis also helps in detecting legacy or deprecated protocols in use, supporting migration efforts and compliance initiatives. When abnormal or unauthorized protocol behavior is detected, such as unexpected port usage or failed authentication sequences, security teams can rapidly investigate and mitigate potential threats.
3. Behavioral Analysis
Behavioral analysis establishes a baseline of normal network activity and user behaviors, using this reference point to identify deviations that could indicate security incidents. Using machine learning and statistical modeling, behavioral analysis tools track metrics including typical login times, access locations, data upload/download volumes, and device interaction patterns.
Behavioral analysis is particularly effective for identifying insider threats and compromised accounts, as attackers often act differently from typical users, even when using valid credentials. However, behavioral analysis requires fine-tuning to balance sensitivity and minimize false positives.
4. Anomaly Detection
Anomaly detection is the process of identifying activity that diverges significantly from established baselines or expected network behavior. Unlike signature-based detection, which relies on known indicators of compromise, anomaly detection specializes in catching unknown or zero-day threats by flagging unexpected trends such as traffic spikes, unusual device communications, or rogue application launches.
Statistical rules, heuristics, or AI-driven models compare real-time data against historical norms to surface potential issues. The main advantage of anomaly detection is its ability to surface emerging threats without prior knowledge of attack signatures. However, this approach requires careful calibration to avoid generating excessive false positives, which can lead to alert fatigue.
5. Flow Data Analysis (NetFlow/IPFIX)
Flow data analysis leverages technologies like NetFlow and IPFIX to summarize metadata about network conversations, such as source/destination IPs, ports, protocol types, and byte/packet counts. Instead of processing every packet, flow analysis aggregates communications into flows, allowing organizations to monitor macro-level traffic patterns, identify top talkers, and detect large file transfers or data exfiltration events.
Flow records are lightweight and well-suited for monitoring large, distributed networks in near real time. By correlating flows across multiple interfaces and time windows, network teams can reconstruct end-to-end paths of communication. Flow analysis is particularly useful for trend analysis, capacity planning, and compliance reporting.
6. Passive Monitoring
Passive monitoring involves collecting and analyzing network traffic without actively injecting additional packets or modifying the flow of data. Network taps, port mirroring (SPAN), or dedicated sensors passively capture all packets traversing a segment, providing an accurate and unobtrusive view of network activity.
Analyzing this data helps organizations generate baselines of normal traffic patterns, identify bandwidth hogs or unusual application usage, and investigate performance issues. Passive monitoring is particularly useful for forensics and compliance. However, it can be limited when it comes to encrypted traffic, as it typically cannot inspect the payload of encrypted packets.
7. Active Monitoring
Active monitoring takes a more hands-on approach by generating synthetic traffic or test probes to evaluate network performance or detect issues in real time. Tools like ping, traceroute, and synthetic application transactions are deployed to continuously assess latency, packet loss, and service availability along key network paths.
Active monitoring allows organizations to proactively validate the health and responsiveness of their infrastructure, often pinpointing faults or degradations before end users notice any impact. Active monitoring complements passive techniques by simulating user or application behavior, providing insight into how the network performs under varying loads or conditions.
Common Use Cases for Network Traffic Analysis
Threat Detection and Intrusion Prevention
Network traffic analysis aids in detecting threats that may bypass endpoint or perimeter controls. By continuously inspecting traffic patterns, behaviors, and flows, NTA tools can identify malware infections, lateral movements by attackers, phishing campaigns, and unauthorized access attempts. Correlating multiple data points, such as a spike in traffic from an unfamiliar segment or unexpected protocol use, increases the chances of early detection, enabling rapid response to emerging threats.
The ability to spot indicators of compromise hidden in seemingly benign traffic is a major advantage of NTA over more traditional security controls. Modern attackers frequently employ sophisticated tactics to evade simple blocklists or signature-based detection, but abnormal network behaviors typically leave traces in traffic data.
Incident Response and Forensics
When a security incident is suspected or confirmed, NTA provides the historical and contextual data necessary for effective investigation. Analysts can review captured packets and flow records to trace an attacker’s movements, reconstruct timelines, and establish the scope of compromise. This forensic evidence is useful for determining root causes, understanding the impact, and proving compliance with breach reporting requirements.
In addition, NTA accelerates incident containment by enabling rapid identification of affected systems, communication with malicious infrastructure, or data exfiltration attempts. Detailed traffic analysis helps quantify what was accessed, modified, or stolen, guiding remediation and legal response efforts.
Network Performance Monitoring
Network traffic analysis is equally important for monitoring and maintaining optimal network performance. By capturing and studying traffic flows, administrators gain visibility into bandwidth utilization, application response times, QoS policy effectiveness, and congestion points. These insights inform capacity planning, resource allocation, and proactive fault management.
When performance degradations occur, NTA assists in rapidly pinpointing the root cause, whether it’s a failing device, misconfigured route, or a sudden surge in traffic to a critical system. Continuous monitoring not only supports day-to-day operations but also enables trend analysis for anticipating future needs or potential issues.
IoT and Cloud Security
The rise of IoT devices and cloud-based services has dramatically increased the attack surface and complexity of corporate networks. Many IoT devices are poorly secured, communicate using proprietary or undocumented protocols, and may not support traditional endpoint defenses. NTA bridges this gap by providing visibility into all network-connected assets, regardless of their location or underlying operating systems.
In cloud environments, NTA enables visibility across hybrid and multi-cloud architectures, detecting misconfigurations, risky behavior, or unauthorized data transfers. Monitoring east-west traffic within and between clouds helps identify lateral movement and policy violations, while integration with other security tools closes visibility gaps.
Network Traffic Analysis Best Practices
Organizations should consider the following practices to ensure thorough analysis of all their network traffic.
1. Integrate with SIEM
Integrating NTA with security information and event management (SIEM) platforms improves detection and response capabilities by providing a centralized view of security data. SIEMs aggregate logs and events from diverse sources; when combined with enriched network traffic data from NTA, this enables better correlation, faster incident investigation, and more accurate threat intelligence.
Analysts gain greater context when alerts are tied directly to network activity, supporting more accurate and efficient triage. Successful integration requires proper mapping of NTA data formats to SIEM fields, and a clear understanding of how to structure event correlation rules. Automation of alerting and case management further simplifies response processes.
By leveraging the strengths of both NTA and SIEM technologies, organizations can build layered, defense-in-depth strategies and reduce mean time to detection (MTTD) and mean time to response (MTTR).
2. Automate Incident Response Workflows
Automating incident response workflows based on NTA triggers accelerates detection-to-containment cycles and reduces the burden on security teams. Integrating NTA tools with security orchestration, automation, and response (SOAR) platforms enables real-time action, such as isolating compromised endpoints, blocking malicious traffic, or opening tickets for investigation, the moment anomalies are detected.
Automation helps ensure consistent, repeatable responses to routine threats. For automation to be effective, playbooks should be carefully developed to address known threat scenarios, incorporate verification steps, and escalate to human analysts when required.
Pre-defining thresholds and rules minimizes manual intervention for common incidents while preserving flexibility for more complex or ambiguous cases. The result is faster, more scalable incident management that limits damage and supports continuous improvement.
3. Regularly Update Threat Intelligence Feeds
Integrating up-to-date threat intelligence feeds into NTA systems bolsters network defenses by providing real-time awareness of emerging threat indicators. Current threat intelligence includes known malicious IP addresses, domains, file hashes, and behavioral patterns sourced from security vendors and trusted communities.
When NTA tools cross-reference observed traffic with these feeds, they can automatically flag or block connections to high-risk entities. Updating threat intelligence feeds on a regular basis is essential, as adversaries frequently change tactics and infrastructure.
Automation can ensure the latest data is always available, reducing manual workload and the risk of relying on outdated information. Organizations should select reputable sources, correlate intelligence from multiple providers, and validate the effectiveness of their feeds against actual network activity.
4. Ensure Data Encryption
Encrypting traffic at rest and in transit is fundamental for protecting the confidentiality and integrity of network data. NTA solutions must handle encrypted traffic effectively, balancing the need for deep visibility with legal and privacy considerations.
Organizations should employ strong, industry-standard encryption protocols like TLS, ensuring keys are carefully managed and rotated to prevent unauthorized decryption. While encryption limits the ability of NTA tools to inspect payloads directly, metadata analysis (such as source/destination, volume, and timing) still offers valuable insight for anomaly detection.
Where deep inspection is necessary, organizations should consider decrypting traffic only in controlled, secure environments and ensuring all inspection complies with data protection regulations. Consistently applying encryption minimizes exposure in the event of insider threats or external breaches.
5. Continuously Evaluate and Improve Analysis Models
Continuous improvement of network traffic analysis models ensures that detection capabilities keep pace with evolving networks and threat tactics. Regularly reviewing alert effectiveness, updating detection thresholds, and refining behavioral baselines helps reduce false positives and adapt to new risks.
Effective NTA programs leverage machine learning, analytics, and user feedback to improve accuracy and relevance over time. Collaboration between security, network, and operations teams is crucial for aligning analysis models with real-world business needs and risk profiles.
Gathering lessons learned from past incidents, integrating diverse data sources, and participating in threat intelligence sharing initiatives further improve detection fidelity.
Related content: Read our guide to network automation.
Selector: AI-Driven Network Traffic Analysis
Selector brings a modern approach to Network Traffic Analysis by combining deep observability with AI-powered correlation and context. Unlike traditional tools that generate floods of raw alerts, Selector intelligently analyzes flow data, packet telemetry, and system logs to surface meaningful insights—enabling faster, more accurate threat detection and performance analysis.
Using machine learning and advanced behavioral models, Selector automatically identifies anomalies and suspicious traffic patterns that may signal data exfiltration, lateral movement, or misconfigurations. Its integrated root cause analysis links related events across the network, providing security and operations teams with a clear path to remediation without requiring packet-by-packet investigation.
Selector’s natural language interface (Copilot) lets teams query network traffic, incidents, or anomalies in plain English, directly from collaboration tools like Slack or Microsoft Teams. Combined with seamless integration into SIEM, SOAR, and NDR pipelines, Selector helps organizations reduce alert fatigue, accelerate response, and improve network-wide visibility across hybrid and cloud environments.
Learn more about how Selector’s AIOps platform can transform your IT operations.
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